A widespread internal brain state for fentanyl withdrawal.

Opioid addiction is characterized by escalating drug use, driven in part by negative reinforcement from withdrawal, but the neural processes linking withdrawal to increased drug-taking remain poorly understood. Here, we use multisite local field potential recordings and interpretable machine learning to identify large-scale brain networks engaged by repeated opioid exposure and withdrawal. After discovering that repeated fentanyl exposure induces a progressively ramping network of widespread high beta and low gamma oscillations, we then identified a distinct brain network that selectively encodes the emergence and severity of opioid withdrawal. This network, termed EN-Withdrawal, is characterized by regional gamma oscillations and widely synchronized delta/theta oscillations. Its activity patterns predict the emergence of spontaneous and naloxone-precipitated withdrawal across multiple independent cohorts, generalizing across mice, sex, opioids, and dosing regimens, while persisting over multiple days of withdrawal. Using a novel, data-driven severity index, we find that network activity scales with individual behavioral severity without simply reflecting ongoing somatic behaviors or general aversion, suggesting that EN-Withdrawal underlies a withdrawal-induced internal state. Strikingly, network activity predicts the escalation of fentanyl self-administration on a mouse-by-mouse basis in experienced, but not drug-naïve, animals. These findings reveal a neurophysiological substrate of the negative reinforcement cycle of addiction that shapes individual vulnerability.